Preserving Old Ontario Barns in the On-Line World: Part 4 of 5, Creating an Informative Database

by Hugh Fraser

This blog appears throughout November and December 2020 with the following topics that build upon each other. They explore some reasons why barn databases may have failed in the past, how OBP is trying to find ways to make it simpler, and to enlist a small army of people to do the work. If something in the blog sparks your good ideas, leave us a message by adding your comment below.

Part 1 of 5: Introduction

Part 2 of 5: What’s an old barn, and why are they difficult to document?

Part 3 of 5: Creating a consistent database

Part 4 of 5: Creating an informative database

Part 5 of 5: Creating a secure, centralized and sustainable database

Databases are only as good as the information fed into them. There is objective data that is easier to describe with numbers such as measurements, or the number of a certain feature; subjective data that is more difficult to determine such as the profile of features, or the condition of a barn; and observational data that varies widely between barns such as carvings, dormers, lightning rods, etc. However, if data collectors are given many choices in drop-down menus on some simple software, good information should result.

For example, objective data might include queries about a barn’s outside length, width, wall height, etc. with drop-down menu options in increments of feet, or inches, because these barns were built before the metric system was adopted. Other objective data includes questions about sizes of beams, posts, other members, roof slopes, number of bents, etc.

Objective data includes numbers that can be determined such as the length of a barn. By showing exactly where measurements should be taken, better information should result. (Schematic courtesy of John Johnson)

Subjective data might include queries about a barn’s condition such as good, fair, poor, or in ruins, with drop-down menu options. Descriptive information would assist in making the correct choice.

To assist with subjective data such as overall condition of a barn, ‘good’ might be described as the barn being square, weatherproof, with no water damage, with most of its original components, well looked after, and no sign of wildlife inside such as raccoons.

Observational data includes those features one might find on the roof of a barn, or its walls, or inside. There are so many of these and they vary so widely, it can be overwhelming just to describe them. However, again, if drop-down menus of the observational data we already know about are included, data collectors can simply check them off as being present.

This fertility symbol appears in many old barns, so showing a photo of different types would be helpful for data collectors to understand and recognize when they choose from a drop-down menu of choices.

Next time, we will explore how to create a secure, centralized and sustainable database

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